# Recommendation Systems Usage Model¶

A typical workflow for methods of recommendation systems includes training and prediction, as explained below.

## Algorithm-Specific Parameters¶

The parameters used by recommender algorithms at each stage depend on a specific algorithm. For a list of these parameters, refer to the description of an appropriate recommender algorithm.

## Training Stage¶

At the training stage, recommender algorithms accept the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

data

Pointer to the $$m \times n$$ numeric table with the mining data.

Note

This table can be an object of any class derived from NumericTable except PackedTriangularMatrix and PackedSymmetricMatrix.

At the training stage, recommender algorithms calculate the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

model

Model with initialized item factors.

Note

The result can only be an object of the Model class.

## Prediction Stage¶

At the prediction stage, recommender algorithms accept the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

model

Model with initialized item factors.

Note

This input can only be an object of the Model class.

At the prediction stage, recommender algorithms calculate the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

prediction

Pointer to the $$m \times n$$ numeric table with predicted ratings.

Note

By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.